A raster is a matrix of cells, or pixels, organized into rows and columns. Each cell contains a value representing some piece of information to be displayed on an interface. Data stored in a raster format may be used to represent specific features such as soil or land-use data, continuous data such as temperature, elevation, or spectral data collected from satellite images and aerial photographs, and maps.
Some rasters have a single band of data measuring a single characteristic, while others have multiple bands. A band is represented by a single matrix of cell values, and a raster with multiple bands contains multiple spatially-coincident matrices of cell values representing the same spatial area. Most satellite images are comprised of multiple bands, and contain values within a range or band of the electromagnetic spectrum. When there are multiple bands, every cell location has more than one value associated with it.
Single-band raster datasets are often classified by partitioning the data into a number of categories, with an equal number of units in each category. Categories with equal numbers of units are referred to as quantiles. In order to determine the range of values that each quantile covers, the data may be sorted and then divided into equal partitions. The lowest and highest value in each quantile forms the range of values. Without any loss of generality, the maximum value of each quantile is retained. The sequence of these maximum values is known as quantile breaks. These quantile breaks are used in the process of colorizing raster data for display.
Classification of multi-band raster datasets poses several challenges. Because the data is comprised of multiple values, it is unclear how to order them such that they can be sorted. It is also unclear how they can be colorized to be displayed on an interface such as a display screen. Existing solutions to these problems involve specifying a function that maps each multi-valued datum into a single numerical value that can be ordered. However, as with single-band break calculations, all data must be retrieved and processed in order to calculate the quantile breaks. One persistent problem with existing techniques is that this data transfer requires substantial processing, and therefore limits performance for large raster datasets.